提出新方法处理带删失数据的期望回归，性能接近完整数据模型。
Neural Networks for Censored Expectile Regression Based on Data Augmentation
- 基于数据增强构建全数据驱动的期望回归神经网络
- 模拟与真实数据均显示其预测性能优于现有删失方法
- 无需指定参数模型，适用于多种删失场景
期望回归神经网络（ERNN）是捕捉数据异质性和复杂非线性结构的强大工具。然而，现有研究大多聚焦于完全观测数据，对删失观测情形关注较少。本文提出一种基于数据增强的ERNN算法——DAERNN，用于建模具有异质性的删失数据。该方法完全数据驱动，假设极少，灵活性强。仿真研究和真实数据应用表明，DAERNN在预测性能上优于现有删失ERNN方法，且表现接近在完整数据上训练的模型。此外，该算法提供统一框架以应对各类删失机制，无需显式指定参数模型，显著提升了实际删失数据分析的适用性。
原文摘要 · Abstract (English)
Expectile regression neural networks (ERNNs) are powerful tools for capturing heterogeneity and complex nonlinear structures in data. However, most existing research has primarily focused on fully observed data, with limited attention paid to scenarios involving censored observations. In this paper, we propose a data augmentation based ERNNs algorithm, termed DAERNN, for modeling heterogeneous censored data. The proposed DAERNN is fully data driven, requires minimal assumptions, and offers substantial flexibility. Simulation studies and real data applications demonstrate that DAERNN outperforms existing censored ERNNs methods and achieves predictive performance comparable to models trained on fully observed data. Moreover, the algorithm provides a unified framework for handling various censoring mechanisms without requiring explicit parametric model specification, thereby enhancing its applicability to practical censored data analysis.
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